etc. …..
#Install packages
install.packages("haven")
trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.3/haven_2.5.3.tgz'
Content type 'application/x-gzip' length 1132879 bytes (1.1 MB)
==================================================
downloaded 1.1 MB
The downloaded binary packages are in
/var/folders/8t/cf05ypcs56z1d0qh3cl43l880000gn/T//RtmpgK7aj1/downloaded_packages
#Load packages
library(data.table)
library(tidyr)
library(haven)
#Add data WV6
WV6_data <- load("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/WV6_Data_R_v20201117.rdata")
WV6_data <- WV6_Data_R_v20201117
print(WV6_data)
#Name the variables
data6 = WV6_Data_R_v20201117[, c("V1", "V2", "V10", "V23", "V70", "V71","V72","V73", "V74B", "V75", "V76", "V77", "V78", "V79", "V147", "V149","V198", "V199", "V201", "V208","V209", "V210", "V240", "V242", "V248", "V229", "V57", "V58")]
names(data6) = c("v1_wave", "v2_country", "v10_happyness", "v23_satisfaction", "v70_creativity", "v71_money", "v72_security", "v73_goodtime", "v74b_help", "v75_success", "v76_risk", "v77_proper", "v78_environment", "v79_tradition", "v147_religion", "v149_hell", "v198_gov_benefits", "v199_fare_public_transportation", "v201_cheating_taxes", "v208_beating_wife", "v209_beating_children", "v210_violence", "v240_sex", "v242_age", "v248_education", "v229_employed", "v57_married", "v58_children")
#Exlusion of participants which no info about sex, age, education,
employment status, marital status, or number of children
data6 = subset(data6, v76_risk > 0 & v240_sex > 0 & v242_age >0 & v248_education > 0 & v229_employed > 0 & v57_married > 0 & v58_children >= 0)
#New column for variable “creativity”
# Erstellen einer neuen Zeile für die Variable
data6$creativity <- NA
# Zuordnen der Werte
data6$creativity[data6$v70_creativity == 1] <- "Very much like me"
data6$creativity[data6$v70_creativity == 2] <- "Like me"
data6$creativity[data6$v70_creativity == 3] <- "Somewhat like me"
data6$creativity[data6$v70_creativity == 4] <- "A little like me"
data6$creativity[data6$v70_creativity == 5] <- "Not like me"
data6$creativity[data6$v70_creativity == 6] <- "Not at all like me"
#New column for variable “security”
data6$creativity <- NA
data6$security[data6$v72_security == 1] <- "Very much like me"
data6$security[data6$v72_security == 2] <- "Like me"
data6$security[data6$v72_security == 3] <- "Somewhat like me"
data6$security[data6$v72_security == 4] <- "A little like me"
data6$security[data6$v72_security == 5] <- "Not like me"
data6$security[data6$v72_security == 6] <- "Not at all like me"
#New column for variable “goodtime”
data6$goodtime <- NA
data6$goodtime[data6$v73_goodtime == 1] <- "Very much like me"
data6$goodtime[data6$v73_goodtime == 2] <- "Like me"
data6$goodtime[data6$v73_goodtime == 3] <- "Somewhat like me"
data6$goodtime[data6$v73_goodtime == 4] <- "A little like me"
data6$goodtime[data6$v73_goodtime == 5] <- "Not like me"
data6$goodtime[data6$v73_goodtime == 6] <- "Not at all like me"
#New column for variable “money”
data6$money <- NA
data6$money[data6$v70_creativity == 1] <- "Very much like me"
data6$money[data6$v70_creativity == 2] <- "Like me"
data6$money[data6$v70_creativity == 3] <- "Somewhat like me"
data6$money[data6$v70_creativity == 4] <- "A little like me"
data6$money[data6$v70_creativity == 5] <- "Not like me"
data6$money[data6$v70_creativity == 6] <- "Not at all like me"
#New column for variable “help”
data6$help <- NA
data6$help[data6$v74b_help == 1] <- "Very much like me"
data6$help[data6$v74b_help == 2] <- "Like me"
data6$help[data6$v74b_help == 3] <- "Somewhat like me"
data6$help[data6$v74b_help == 4] <- "A little like me"
data6$help[data6$v74b_help == 5] <- "Not like me"
data6$help[data6$v74b_help == 6] <- "Not at all like me"
#New column for variable “success”
data6$success <- NA
data6$success[data6$v75_success == 1] <- "Very much like me"
data6$success[data6$v75_success == 2] <- "Like me"
data6$success[data6$v75_success == 3] <- "Somewhat like me"
data6$success[data6$v75_success == 4] <- "A little like me"
data6$success[data6$v75_success == 5] <- "Not like me"
data6$success[data6$v75_success == 6] <- "Not at all like me"
#New column for variable “risk”
data6$risk <- NA
# Zuordnen der Werte
data6$risk[data6$v76_risk == 1] <- "Very much like me"
data6$risk[data6$v76_risk == 2] <- "Like me"
data6$risk[data6$v76_risk == 3] <- "Somewhat like me"
data6$risk[data6$v76_risk == 4] <- "A little like me"
data6$risk[data6$v76_risk == 5] <- "Not like me"
data6$risk[data6$v76_risk == 6] <- "Not at all like me"
#New column for variable “proper”
data6$proper <- NA
data6$proper[data6$v77_proper == 1] <- "Very much like me"
data6$proper[data6$v77_proper == 2] <- "Like me"
data6$proper[data6$v77_proper == 3] <- "Somewhat like me"
data6$proper[data6$v77_proper == 4] <- "A little like me"
data6$proper[data6$v77_proper == 5] <- "Not like me"
data6$proper[data6$v77_proper == 6] <- "Not at all like me"
#New column for variable “environment”
data6$environment <- NA
data6$environment[data6$v78_environment == 1] <- "Very much like me"
data6$environment[data6$v78_environment == 2] <- "Like me"
data6$environment[data6$v78_environment == 3] <- "Somewhat like me"
data6$environment[data6$v78_environment == 4] <- "A little like me"
data6$environment[data6$v78_environment == 5] <- "Not like me"
data6$environment[data6$v78_environment == 6] <- "Not at all like me"
#New column for variable “tradition”
data6$tradition <- NA
data6$tradition[data6$v79_tradition == 1] <- "Very much like me"
data6$tradition[data6$v79_tradition == 2] <- "Like me"
data6$tradition[data6$v79_tradition == 3] <- "Somewhat like me"
data6$tradition[data6$v79_tradition == 4] <- "A little like me"
data6$tradition[data6$v79_tradition == 5] <- "Not like me"
data6$tradition[data6$v79_tradition == 6] <- "Not at all like me"
#New column for variable “satisfaction”
data6$satisfaction <- NA
data6$satisfaction[data6$v57_married == 1] <- "Very happy"
data6$satisfaction[data6$v10_happyness == 2] <- "Rather happy"
data6$satisfaction[data6$v10_happyness == 3] <- "Not very happy"
data6$satisfaction[data6$v10_happyness == 4] <- "Not at all happy"
#New column for variable “marrital status”
# Erstellen einer neuen Zeile für die Variable "verheiratet"
data6$marital_status <- NA
# Zuordnen der Werte
data6$marital_status[data6$v57_married == 1] <- "Married"
data6$marital_status[data6$v57_married == 2] <- "Living together as married"
data6$marital_status[data6$v57_married == 3] <- "Divorced"
data6$marital_status[data6$v57_married == 4] <- "Separated"
data6$marital_status[data6$v57_married == 5] <- "Widowed"
data6$marital_status[data6$v57_married == 6] <- "Single"
#New column for variable “religion”
# Erstellen einer neuen Zeile für die Variable
data6$religion <- NA
# Zuordnen der Werte
data6$religion[data6$v147_religion == 1] <- "A religious person"
data6$religion[data6$v147_religion == 2] <- "Not a religious person"
data6$religion[data6$v147_religion == 3] <- "An atheist"
#Categorical age variable (for example, to plot response frequencies
by category)
data6$agecat <- NA # Create an empty column 'agecat'
data6$agecat[data6$v242_age < 20] = "15-19"
data6$agecat[data6$v242_age >= 20 & data6$v242_age < 30] = "20-29"
data6$agecat[data6$v242_age >= 30 & data6$v242_age < 40] = "30-39"
data6$agecat[data6$v242_age >= 40 & data6$v242_age < 50] = "40-49"
data6$agecat[data6$v242_age >= 50 & data6$v242_age < 60] = "50-59"
data6$agecat[data6$v242_age >= 60 & data6$v242_age < 70] = "60-69"
data6$agecat[data6$v242_age >= 70 & data6$v242_age < 80] = "70-79"
data6$agecat[data6$v242_age >= 80] = "80+"
#New column for variable “sex”
# Erstellen einer neuen Zeile für die Variable
data6$sex <- NA
# Zuordnen der Werte
data6$sex[data6$v240_sex == 1] <- "male"
data6$sex[data6$v240_sex == 2] <- "female"
#Number of countries
length(unique(data6$v2_country))
#Number of participants
nrow(data6)
#Range of age
range(data6$v242_age)
#Table about gender
table(data6$sex)
Overview of religion vs. Risk
cross_table <- table(data6$religion, data6$risk)
print(cross_table)
A little like me Like me Not at all like me Not like me Somewhat like me
A religious person 8338 8617 8905 12174 10144
An atheist 718 612 507 1031 689
Not a religious person 3790 2887 2851 4944 3678
Very much like me
A religious person 6643
An atheist 335
Not a religious person 1804
#compare the groups religion and risk ### Because of the different
sample sizes to compare the groups, the data is normalised and changed
into % to compare the groups.
# Summe of participants in each group
sum_religious <- sum(c(8338, 718, 3790))
sum_atheist <- sum(c(8617, 612, 2887))
sum_not_religious <- sum(c(8905, 507, 2851))
# normalising data
normalized_data <- data.frame(
Religion = c("A religious person", "An atheist", "Not a religious person"),
`A little like me` = c(8338/sum_religious, 718/sum_atheist, 3790/sum_not_religious),
`Like me` = c(8617/sum_religious, 612/sum_atheist, 2887/sum_not_religious),
`Not at all like me` = c(8905/sum_religious, 507/sum_atheist, 2851/sum_not_religious),
`Not like me` = c(12174/sum_religious, 1031/sum_atheist, 4944/sum_not_religious),
`Somewhat like me` = c(10144/sum_religious, 689/sum_atheist, 3678/sum_not_religious),
`Very much like me` = c(6643/sum_religious, 335/sum_atheist, 1804/sum_not_religious)
)
# Convert data to long format
normalized_data_long <- tidyr::gather(normalized_data, key = "Risk", value = "Percentage", -Religion)
# Create a bar chart
ggplot(normalized_data_long, aes(x = Religion, y = Percentage, fill = Risk)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Normalized Risk Distribution by Religion",
x = "Religion",
y = "Percentage")

---
title: "R Notebook"
output: html_notebook
---

#### To Do: 
# 1. Load 
# 2. Key variables
# 3. descriptives --> how many countries, participants 
# 4. How to visualize? do we want to do the visualization? Per Country or all together // coulors? etc. 
# 5. What do we want to present? 
- Presentation 19.12.2023 
- finalise presentation 05.12.2023 with rui
- Send presentation 27.11.2023 
- Presentation content 
  - why we do this 
  - research gap 
  - method 
  - results
  - challenges 
  - conclusion 
  ---> Do the Presentation on GoogleSlides 
- cancel 12.12.2023 und 19.12.2023 

# etc. ..... 

#Install packages 
```{r}
install.packages("haven")
```

#Load packages 
```{r}
library(data.table)
library(tidyr)
library(haven)
library(ggplot2)
```


#Add data WV6
```{r}
WV6_data <- load("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 
print(WV6_data)
```
#Name the variables 
```{r}
data6 = WV6_Data_R_v20201117[, c("V1", "V2", "V10", "V23", "V70", "V71","V72","V73", "V74B", "V75", "V76", "V77", "V78",  "V79", "V147", "V149","V198", "V199", "V201", "V208","V209", "V210", "V240", "V242", "V248", "V229", "V57", "V58")]

names(data6) = c("v1_wave", "v2_country", "v10_happyness", "v23_satisfaction", "v70_creativity", "v71_money", "v72_security", "v73_goodtime", "v74b_help", "v75_success", "v76_risk", "v77_proper", "v78_environment", "v79_tradition", "v147_religion", "v149_hell", "v198_gov_benefits", "v199_fare_public_transportation", "v201_cheating_taxes", "v208_beating_wife", "v209_beating_children", "v210_violence", "v240_sex", "v242_age", "v248_education", "v229_employed", "v57_married", "v58_children")
```


#Exlusion of participants which no info about sex, age, education, employment status, marital status, or number of children
```{r}
data6 = subset(data6, v76_risk > 0 & v240_sex > 0 & v242_age >0 & v248_education > 0 & v229_employed > 0 & v57_married > 0 & v58_children >= 0)
```


#New column for variable "creativity"
```{r}
# Erstellen einer neuen Zeile für die Variable 
data6$creativity <- NA

# Zuordnen der Werte
data6$creativity[data6$v70_creativity == 1] <- "Very much like me"
data6$creativity[data6$v70_creativity == 2] <- "Like me"
data6$creativity[data6$v70_creativity == 3] <- "Somewhat like me"
data6$creativity[data6$v70_creativity == 4] <- "A little like me"
data6$creativity[data6$v70_creativity == 5] <- "Not like me"
data6$creativity[data6$v70_creativity == 6] <- "Not at all like me"
```

#New column for variable "security"
```{r}
data6$creativity <- NA

data6$security[data6$v72_security == 1] <- "Very much like me"
data6$security[data6$v72_security == 2] <- "Like me"
data6$security[data6$v72_security == 3] <- "Somewhat like me"
data6$security[data6$v72_security == 4] <- "A little like me"
data6$security[data6$v72_security == 5] <- "Not like me"
data6$security[data6$v72_security == 6] <- "Not at all like me"
```

#New column for variable "goodtime"
```{r}
data6$goodtime <- NA

data6$goodtime[data6$v73_goodtime == 1] <- "Very much like me"
data6$goodtime[data6$v73_goodtime == 2] <- "Like me"
data6$goodtime[data6$v73_goodtime == 3] <- "Somewhat like me"
data6$goodtime[data6$v73_goodtime == 4] <- "A little like me"
data6$goodtime[data6$v73_goodtime == 5] <- "Not like me"
data6$goodtime[data6$v73_goodtime == 6] <- "Not at all like me"
```

#New column for variable "money"
```{r}
data6$money <- NA

data6$money[data6$v70_creativity == 1] <- "Very much like me"
data6$money[data6$v70_creativity == 2] <- "Like me"
data6$money[data6$v70_creativity == 3] <- "Somewhat like me"
data6$money[data6$v70_creativity == 4] <- "A little like me"
data6$money[data6$v70_creativity == 5] <- "Not like me"
data6$money[data6$v70_creativity == 6] <- "Not at all like me"
```

#New column for variable "help"
```{r}
data6$help <- NA

data6$help[data6$v74b_help == 1] <- "Very much like me"
data6$help[data6$v74b_help == 2] <- "Like me"
data6$help[data6$v74b_help == 3] <- "Somewhat like me"
data6$help[data6$v74b_help == 4] <- "A little like me"
data6$help[data6$v74b_help == 5] <- "Not like me"
data6$help[data6$v74b_help == 6] <- "Not at all like me"
```

#New column for variable "success"
```{r}
data6$success <- NA

data6$success[data6$v75_success == 1] <- "Very much like me"
data6$success[data6$v75_success == 2] <- "Like me"
data6$success[data6$v75_success == 3] <- "Somewhat like me"
data6$success[data6$v75_success == 4] <- "A little like me"
data6$success[data6$v75_success == 5] <- "Not like me"
data6$success[data6$v75_success == 6] <- "Not at all like me"
```


#New column for variable "risk"
```{r}
data6$risk <- NA

data6$risk[data6$v76_risk == 1] <- "Very much like me"
data6$risk[data6$v76_risk == 2] <- "Like me"
data6$risk[data6$v76_risk == 3] <- "Somewhat like me"
data6$risk[data6$v76_risk == 4] <- "A little like me"
data6$risk[data6$v76_risk == 5] <- "Not like me"
data6$risk[data6$v76_risk == 6] <- "Not at all like me"
```

#New column for variable "proper"
```{r}
data6$proper <- NA

data6$proper[data6$v77_proper == 1] <- "Very much like me"
data6$proper[data6$v77_proper == 2] <- "Like me"
data6$proper[data6$v77_proper == 3] <- "Somewhat like me"
data6$proper[data6$v77_proper == 4] <- "A little like me"
data6$proper[data6$v77_proper == 5] <- "Not like me"
data6$proper[data6$v77_proper == 6] <- "Not at all like me"
```

#New column for variable "environment"
```{r}
data6$environment <- NA

data6$environment[data6$v78_environment == 1] <- "Very much like me"
data6$environment[data6$v78_environment == 2] <- "Like me"
data6$environment[data6$v78_environment == 3] <- "Somewhat like me"
data6$environment[data6$v78_environment == 4] <- "A little like me"
data6$environment[data6$v78_environment == 5] <- "Not like me"
data6$environment[data6$v78_environment == 6] <- "Not at all like me"
```


#New column for variable "tradition"
```{r}
data6$tradition <- NA

data6$tradition[data6$v79_tradition == 1] <- "Very much like me"
data6$tradition[data6$v79_tradition == 2] <- "Like me"
data6$tradition[data6$v79_tradition == 3] <- "Somewhat like me"
data6$tradition[data6$v79_tradition == 4] <- "A little like me"
data6$tradition[data6$v79_tradition == 5] <- "Not like me"
data6$tradition[data6$v79_tradition == 6] <- "Not at all like me"
```

#New column for variable "satisfaction"
```{r}
data6$satisfaction <- NA

data6$satisfaction[data6$v57_married == 1] <- "Very happy"
data6$satisfaction[data6$v10_happyness == 2] <- "Rather happy"
data6$satisfaction[data6$v10_happyness == 3] <- "Not very happy"
data6$satisfaction[data6$v10_happyness == 4] <- "Not at all happy"
```

#New column for variable "marrital status"

```{r}
data6$marital_status <- NA

data6$marital_status[data6$v57_married == 1] <- "Married"
data6$marital_status[data6$v57_married == 2] <- "Living together as married"
data6$marital_status[data6$v57_married == 3] <- "Divorced"
data6$marital_status[data6$v57_married == 4] <- "Separated"
data6$marital_status[data6$v57_married == 5] <- "Widowed"
data6$marital_status[data6$v57_married == 6] <- "Single"
```

#New column for variable "religion"
```{r}
data6$religion <- NA

data6$religion[data6$v147_religion == 1] <- "A religious person"
data6$religion[data6$v147_religion == 2] <- "Not a religious person"
data6$religion[data6$v147_religion == 3] <- "An atheist"
```


#Categorical age variable (for example, to plot response frequencies by category)
```{r}
data6$agecat <- NA  # Create an empty column 'agecat'

data6$agecat[data6$v242_age < 20] = "15-19"
data6$agecat[data6$v242_age >= 20 & data6$v242_age < 30] = "20-29"
data6$agecat[data6$v242_age >= 30 & data6$v242_age < 40] = "30-39"
data6$agecat[data6$v242_age >= 40 & data6$v242_age < 50] = "40-49"
data6$agecat[data6$v242_age >= 50 & data6$v242_age < 60] = "50-59"
data6$agecat[data6$v242_age >= 60 & data6$v242_age < 70] = "60-69"
data6$agecat[data6$v242_age >= 70 & data6$v242_age < 80] = "70-79"
data6$agecat[data6$v242_age >= 80] = "80+"
```

#New column for variable "sex"
```{r}
data6$sex <- NA 

data6$sex[data6$v240_sex == 1] <- "male"
data6$sex[data6$v240_sex == 2] <- "female"
```

#Number of countries
```{r}
length(unique(data6$v2_country)) 
```

#Number of participants
```{r}
nrow(data6) 
```

#Range of age
```{r}
range(data6$v242_age) 
```

#Table about gender
```{r}
table(data6$sex)
```

# Scatterplot of Age vs. Risk with ggplot
```{r}
data_df <- data.frame(Age = data6$v242_age, Risk = data6$v76_risk) # Convert data into a data frame.

ggplot(data_df, aes(x = Age, y = Risk)) +
  geom_point(color = "blue", size = 1) +
  labs(title = "Scatterplot of Age vs. Risk",
       x = "Age",
       y = "Risk")
```

# Histogramm for Age
```{r}
hist(data6$v242_age, 
     main="Häufigkeitsverteilung des Alters",
     xlab="Alter",
     ylab="Häufigkeit",
     col="blue")
```


# Overview of religion vs. Risk
```{r}
cross_table <- table(data6$religion, data6$risk)
print(cross_table)
```

#compare the groups religion and risk 
### Because of the different sample sizes to compare the groups, the data is normalised and changed into % to compare the groups. 
```{r}
# Summe of participants in each group
sum_religious <- sum(c(8338, 718, 3790))
sum_atheist <- sum(c(8617, 612, 2887))
sum_not_religious <- sum(c(8905, 507, 2851))

# normalising data
normalized_data <- data.frame(
  Religion = c("A religious person", "An atheist", "Not a religious person"),
  `A little like me` = c(8338/sum_religious, 718/sum_atheist, 3790/sum_not_religious),
  `Like me` = c(8617/sum_religious, 612/sum_atheist, 2887/sum_not_religious),
  `Not at all like me` = c(8905/sum_religious, 507/sum_atheist, 2851/sum_not_religious),
  `Not like me` = c(12174/sum_religious, 1031/sum_atheist, 4944/sum_not_religious),
  `Somewhat like me` = c(10144/sum_religious, 689/sum_atheist, 3678/sum_not_religious),
  `Very much like me` = c(6643/sum_religious, 335/sum_atheist, 1804/sum_not_religious)
)

# Convert data to long format
normalized_data_long <- tidyr::gather(normalized_data, key = "Risk", value = "Percentage", -Religion)

# Create a bar chart
ggplot(normalized_data_long, aes(x = Religion, y = Percentage, fill = Risk)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Normalized Risk Distribution by Religion",
       x = "Religion",
       y = "Percentage")

```









